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Journal ArticleDOI

A Taxonomy of Recommender Agents on theInternet

01 Jun 2003-Artificial Intelligence Review (Kluwer Academic Publishers)-Vol. 19, Iss: 4, pp 285-330
TL;DR: A state-of-the-art taxonomy of intelligent recommender agents on the Internet and a cross-dimensional analysis with the aim of providing a starting point for researchers to construct their own recommender system.
Abstract: Recently, Artificial Intelligence techniques have proved useful in helping users to handle the large amount of information on the Internet. The idea of personalized search engines, intelligent software agents, and recommender systems has been widely accepted among users who require assistance in searching, sorting, classifying, filtering and sharing this vast quantity of information. In this paper, we present a state-of-the-art taxonomy of intelligent recommender agents on the Internet. We have analyzed 37 different systems and their references and have sorted them into a list of 8 basic dimensions. These dimensions are then used to establish a taxonomy under which the systems analyzed are classified. Finally, we conclude this paper with a cross-dimensional analysis with the aim of providing a starting point for researchers to construct their own recommender system.

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Citations
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Book ChapterDOI
01 Jan 2011
TL;DR: The main goal is to delineate, in a coherent and structured way, the chapters included in this handbook and to help the reader navigate the extremely rich and detailed content that the handbook offers.
Abstract: Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user. In this introductory chapter we briefly discuss basic RS ideas and concepts. Our main goal is to delineate, in a coherent and structured way, the chapters included in this handbook and to help the reader navigate the extremely rich and detailed content that the handbook offers.

2,160 citations


Cites background from "A Taxonomy of Recommender Agents on..."

  • ...The most complex items that have been considered are insurance policies, financial investments, travels, jobs [72]....

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  • ...[72] provide a taxonomy of RSs and classify existing RS applications to specific application domains....

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Book ChapterDOI
01 Jan 2011
TL;DR: The role of User Generated Content is described as a way for taking into account evolving vocabularies, and the challenge of feeding users with serendipitous recommendations, that is to say surprisingly interesting items that they might not have otherwise discovered.
Abstract: Recommender systems have the effect of guiding users in a personal- ized way to interesting objects in a large space of possible options. Content-based recommendation systems try to recommend items similar to those a given user has liked in the past. Indeed, the basic process performed by a content-based recom- mender consists in matching up the attributes of a user profile in which preferences and interests are stored, with the attributes of a content object (item), in order to recommend to the user new interesting items. This chapter provides an overview of content-based recommender systems, with the aim of imposing a degree of order on the diversity of the different aspects involved in their design and implementation. The first part of the chapter presents the basic concepts and terminology of content- based recommender systems, a high level architecture, and their main advantages and drawbacks. The second part of the chapter provides a review of the state of the art of systems adopted in several application domains, by thoroughly describ- ing both classical and advanced techniques for representing items and user profiles. The most widely adopted techniques for learning user profiles are also presented. The last part of the chapter discusses trends and future research which might lead towards the next generation of systems, by describing the role of User Generated Content as a way for taking into account evolving vocabularies, and the challenge of feeding users with serendipitous recommendations, that is to say surprisingly interesting items that they might not have otherwise discovered.

1,582 citations


Additional excerpts

  • ...A thorough review is presented in [64, 69, 82]....

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Journal ArticleDOI
TL;DR: This work provides a creditable method for compressing bipartite networks, and highlights a possible way for the better solution of a long-standing challenge in modern information science: How to do a personal recommendation.
Abstract: One-mode projecting is extensively used to compress bipartite networks. Since one-mode projection is always less informative than the bipartite representation, a proper weighting method is required to better retain the original information. In this article, inspired by the network-based resource-allocation dynamics, we raise a weighting method which can be directly applied in extracting the hidden information of networks, with remarkably better performance than the widely used global ranking method as well as collaborative filtering. This work not only provides a creditable method for compressing bipartite networks, but also highlights a possible way for the better solution of a long-standing challenge in modern information science: How to do a personal recommendation.

1,040 citations

Journal ArticleDOI
TL;DR: A conceptual model with 28 propositions derived from five theoretical perspectives is developed that identifies other important aspects of RAs, namely RA use, RA characteristics, provider credi'r, and user-RA interaction, which influence users' decision-making processes and outcomes, as well as their evaluation of RA.
Abstract: Recommendation agents (RAs) are software agents that elicit the interests or preferences of individual consumers for products, either explicitly or implicitly, and make recommendations accordingly RAs have the potential to support and improve the quality of the decisions consumers make when searching for and selecting products online They can reduce the information overload facing consumers, as well as the complexity of online searches Prior research on RAs has focused mostly on developing and evaluating different underlying algorithms that generate recommendations This paper instead identifies other important aspects of RAs, namely RA use, RA characteristics, provider credi'r, and user-RA interaction, which influence users' decision-making processes and outcomes, as well as their evaluation of RAs It goes beyond generalized models, such as TAM, and identifies the RA-specific features, such as RA input, process, and output design characteristics, that affect users' evaluations, including their assessments of the usefulness and ease-of-use of RA applications Based on a review of existing literature on e-commerce RAs, this paper develops a conceptual model with 28 propositions derived from five theoretical perspectives The propositions help answer the two research questions: (1) How do RA use, RA characteristics, and other factors influence consumer decision making processes and outcomes? (2) How do RA use, RA characteristics, and other factors influence users' evaluations of RAs? By identifying the critical gaps between what we know and what we need to know, this paper identifies potential areas of future research for scholars It also provides advice to information systems practitioners concerning the effective design and development of RAs

968 citations


Cites background from "A Taxonomy of Recommender Agents on..."

  • ...Similarly, the majority of the review articles regarding RAs (Herlocker et al. 2004; Montaner et al. 2003; Sarwar et al. 2000; Schafer et al. 2001; Zhang 2002) provide either evaluations of different recommendation-generating algorithms (focusing primarily on criteria such as accuracy and coverage)…...

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Journal ArticleDOI
TL;DR: This study presents an overview of the field of recommender systems with current generation of recommendation methods and examines comprehensively CF systems with its algorithms.
Abstract: Recommender Systems are software tools and techniques for suggesting items to users by considering their preferences in an automated fashion. The suggestions provided are aimed at support users in various decision- making processes. Technically, recommender system has their origins in different fields such as Information Retrieval (IR), text classification, machine learning and Decision Support Systems (DSS). Recommender systems are used to address the Information Overload (IO) problem by recommending potentially interesting or useful items to users. They have proven to be worthy tools for online users to deal with the IO and have become one of the most popular and powerful tools in E-commerce. Many existing recommender systems rely on the Collaborative Filtering (CF) and have been extensively used in E-commerce .They have proven to be very effective with powerful techniques in many famous E-commerce companies. This study presents an overview of the field of recommender systems with current generation of recommendation methods and examines comprehensively CF systems with its algorithms.

949 citations

References
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Book
01 Jan 1973
TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
Abstract: Provides a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition. The topics treated include Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.

13,647 citations

Book
01 Jan 1983
TL;DR: Reading is a need and a hobby at once and this condition is the on that will make you feel that you must read.
Abstract: Some people may be laughing when looking at you reading in your spare time. Some may be admired of you. And some may want be like you who have reading hobby. What about your own feel? Have you felt right? Reading is a need and a hobby at once. This condition is the on that will make you feel that you must read. If you know are looking for the book enPDFd introduction to modern information retrieval as the choice of reading, you can find here.

12,059 citations


"A Taxonomy of Recommender Agents on..." refers background or methods in this paper

  • ...Feature selection can be achieved through different approaches which reduce the number of words: stop-words, pruning, stemming, etc (see Salton and McGill 1983)....

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  • ...An early similarity formula was used by Salton in the SMART system (Salton and McGill 1983)....

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Journal ArticleDOI
TL;DR: This paper summarizes the insights gained in automatic term weighting, and provides baseline single term indexing models with which other more elaborate content analysis procedures can be compared.
Abstract: The experimental evidence accumulated over the past 20 years indicates that textindexing systems based on the assignment of appropriately weighted single terms produce retrieval results that are superior to those obtainable with other more elaborate text representations. These results depend crucially on the choice of effective term weighting systems. This paper summarizes the insights gained in automatic term weighting, and provides baseline single term indexing models with which other more elaborate content analysis procedures can be compared.

9,460 citations


"A Taxonomy of Recommender Agents on..." refers methods in this paper

  • ...Cosine similarity Cosine similarity comes from information retrieval research and is used in systems with simple user profile representation (Salton and Buckley 1988; Buckley et al. 1996; Yan and Garcia-Molina 1995; Chen et al. 2000)....

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Proceedings ArticleDOI
22 Oct 1994
TL;DR: GroupLens is a system for collaborative filtering of netnews, to help people find articles they will like in the huge stream of available articles, and protect their privacy by entering ratings under pseudonyms, without reducing the effectiveness of the score prediction.
Abstract: Collaborative filters help people make choices based on the opinions of other people. GroupLens is a system for collaborative filtering of netnews, to help people find articles they will like in the huge stream of available articles. News reader clients display predicted scores and make it easy for users to rate articles after they read them. Rating servers, called Better Bit Bureaus, gather and disseminate the ratings. The rating servers predict scores based on the heuristic that people who agreed in the past will probably agree again. Users can protect their privacy by entering ratings under pseudonyms, without reducing the effectiveness of the score prediction. The entire architecture is open: alternative software for news clients and Better Bit Bureaus can be developed independently and can interoperate with the components we have developed.

5,644 citations


"A Taxonomy of Recommender Agents on..." refers background or methods in this paper

  • ...− Collaborative filtering systems (Goldberg et al. 1992; Resnick et al. 1994; Shardanand and Maes 1995) which keep a matrix with the user-item ratings as a profile (see Section 3.1.7)....

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  • ...1995), GroupLens (Resnick et al. 1994), CDNow (CDNow 2001) and Amazon (Amazon 2001) also use hybrid relevance feedback....

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  • ...WebWatcher (Joachims et al. 1997), LifeStyle Finder (Krulwich 1997), Krakatoa Chronicle (Kamba et al. 1995), GroupLens (Resnick et al. 1994), CDNow (CDNow 2001) and Amazon (Amazon 2001) also use hybrid relevance feedback....

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  • ...There are three main approaches to get explicit relevance feedback: like/dislike (e.g., Chen et al. 2000; Billsus and Pazzani 1999), ratings (e.g., Shardanand and Maes 1995; Moukas 1997) and text comments (e.g., Resnick et al. 1994; Goldberg 1992)....

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  • ...− Collaborative filtering systems (Goldberg et al. 1992; Resnick et al. 1994; Shardanand and Maes 1995) which keep a matrix with the user-item ratings as a profile (see Section 3....

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